Ants, Bees, Genomes & Evolution @ Queen Mary University London

We use the 15,000 species of ants as a set of model systems to address questions about major transitions, the genetics of behavior, the genomic constraints modulating social evolution, and the molecular mechanisms responsible for differences between castes. We combine animal experiments and laboratory work with population genomics, molecular evolution, and comparative transcriptomics.

For example:

• We discovered that alternate variants of a supergene carried by a pair of “social chromosomes” determine whether fire ant colonies include one or multiple queens (Nature 2013).
• We discovered the degenerative expansion of the Sb fire ant supergene variant: it carries almost 2x as much DNA as the SB variant (MBE 2019). This represents a rarely seen early stage of supergene evolution.
• We disentangled adaptive and neutral forces affecting gene expression in the supergene (eLife 2020). Some genes are socially antagonistic, while others have dosage compensation for the degeneration due to recombination suppression.
• We revealed the introgression of the fire ant social supergene across species boundaries (Nature Communications 2022).
• We found high divergence between supergene variants, and strikingly different patterns of genetic diversity (Molecular Ecology 2017 and Evolution Letters 2017).
• We discovered that another social supergene convergently evolved in a second lineage of ants (Current Biology 2014).
• We reviewed recent research on ants (Current Opinion in Insect Science 2018).
• We sequenced and analyzed the genome of the invasive red fire ant Solenopsis invicta (PNAS 2011).
• We described the gene expression changes that occur in a virgin queen when she has the opportunity to replace her mother (Molecular Ecology 2010).
• We examined selective regime (positive vs. neutral selection) and fate of duplicated genes (conserved vs. subfunctionalized vs. neofunctionalization) for gene families including sex-determination loci (tranfsormer/feminiser), clock genes, chemosensory proteins (CSPs), olfactory binding proteins (OBPs), olfactory receptors and vitellogenin genes.

• Genome Assembly. How do you know which genome assembly software or parameter works best for your species? Our CompareGenomeQualities software can help.
• Comparative Genomics. Our SequenceServer BLAST interface is used by thousands daily and is cited weekly. It enables researchers to:
  • run BLAST on private unpublished datasets;
  • access an intuitive graphical user interface that provides helpful visualization for interpreting genomic comparisons;
  • run BLAST against Uniprot or NR without having to wait in NCBI's queue;
  • help labs or communities share a BLAST server.
• Genome Evolution. We created a framework for simulating genome evolution of the ~15% of animal species with haplo-diploid sex determination systems (Pracana et al 2022).
• Gene Prediction. Correctly identifying genes in a genome sequence remains challenging. This is a huge problem for multi-species multi-gene analyses on emerging model organisms (trash in trash out).
  • GeneValidator identifies problems with gene predictions. Use it to prioritize curation or to choose the best geneset.
  • How can you transfer high quality gene predictions from an old genome assembly to a newer one? Our flo GFF transfer tool helps with that.

We aim to improve pollinator health to protect biodiversity and agriculture. For this, we take inspiration from biomedical approaches for understanding cancer biology and personalized medicine. In particular, we use high-resolution molecular tools including population genomics and gene expression analyses to understand how pollinators are affected by changing environments (e.g., pesticide exposure, habitat loss, changing climates) and their ability to cope with such changes. We aim to give regulators the tools they need to better evaluate the potential impacts of insecticides, and to establish manners of monitoring pollinator health.

• We have outlined a vision of how molecular approaches can help to better evaluate the impacts of pesticides on beneficial pollinators (Trends in Ecology & Evolution 2020)
• We found that two insecticides targeting the same receptor disrupt organismal function in very different manners (Molecular Ecology 2019).
• We find that bumblebee queens and workers are affected differently by the same insecticide (Molecular Ecology 2019).
• We are quantifying the effects of pesticide exposure on gene expression in various bee species.
• By surveying populations of wild Bombus terrestris bumblebees, we have detected the genetic changes that have helped them respond to recent environmental pressures (MBE 2022).
• We have contributed to several experiments examining the impact of pesticides on field colonies.